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Robust hypothesis testing for asymmetric nominal densities under a relative entropy tolerance
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作者 Enbin Song Qingjiang Shi +1 位作者 Yunmin Zhu Jianxi Pan 《Science China Mathematics》 SCIE CSCD 2018年第10期1851-1880,共30页
In this paper, we address an open problem raised by Levy(2009) regarding the design of a binary minimax test without the symmetry assumption on the nominal conditional probability densities of observations. In the bin... In this paper, we address an open problem raised by Levy(2009) regarding the design of a binary minimax test without the symmetry assumption on the nominal conditional probability densities of observations. In the binary minimax test, the nominal likelihood ratio is a monotonically increasing function and the probability densities of the observations are located in neighborhoods characterized by placing a bound on the relative entropy between the actual and nominal densities. The general minimax testing problem at hand is an infinite-dimensional optimization problem, which is quite difficult to solve. In this paper, we prove that the complicated minimax testing problem can be substantially reduced to solve a nonlinear system of two equations having only two unknown variables, which provides an efficient numerical solution. 展开更多
关键词 Kullback-Leibler divergence robust hypothesis testing min-max problem least-favorable densities saddle point
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